Storage system is an important component in many data intensive applications, including data grid. Security,availability, and high performance are important issues in the storage system design. In this paper we presen...
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Storage system is an important component in many data intensive applications, including data grid. Security,availability, and high performance are important issues in the storage system design. In this paper we present a Peer-to-Peer (P2P) storage system design based on distributed hash table (DHT) and short secret sharing (SSS)to provide highly available, secure and efficient data storage services. Existing DHTs do not consider share location and search. Also, storage systems using data partitioning schemes (including SSS) does not consider the severe problems in share update. We develop three access protocols to maintain the share consistency in spite of concurrent update, partial update and compromised storage nodes by storing a limit number of history versions of the shares. We also conducted experimental studies to evaluate the performance and data availability and compare the behaviors of the schemes.
We introduce a model for provable data possession (PDP) that can be used for remote data checking: A client that has stored data at an untrusted server can verify that the server possesses the original data without re...
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We introduce a model for provable data possession (PDP) that can be used for remote data checking: A client that has stored data at an untrusted server can verify that the server possesses the original data without retrieving it. The model generates probabilistic proofs of possession by sampling random sets of blocks from the server, which drastically reduces I/O costs. The client maintains a constant amount of metadata to verify the proof. The challenge/response protocol transmits a small, constant amount of data, which minimizes network communication. Thus, the PDP model for remote data checking is lightweight and supports large data sets in distributed storage systems. The model is also robust in that it incorporates mechanisms for mitigating arbitrary amounts of data corruption. We present two provably-secure PDP schemes that are more efficient than previous solutions. In particular, the overhead at the server is low (or even constant), as opposed to linear in the size of the data. We then propose a generic transformation that adds robustness to any remote data checking scheme based on spot checking. Experiments using our implementation verify the practicality of PDP and reveal that the performance of PDP is bounded by disk I/O and not by cryptographic computation. Finally, we conduct an in-depth experimental evaluation to study the tradeoffs in performance, security, and space overheads when adding robustness to a remote data checking scheme.
We propose an innovative framework to improve communication reliability of IoT sensory traffic, when the network is subject to congestion. The proposed architecture is based on information dispersion and relies on the...
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ISBN:
(数字)9781728146010
ISBN:
(纸本)9781728146027
We propose an innovative framework to improve communication reliability of IoT sensory traffic, when the network is subject to congestion. The proposed architecture is based on information dispersion and relies on the fact that congestion occurs in space-and time-differing parts of the network. Our approach uses encoding and spreading of data flows across various parts of the network, while ensuring that the original data could be recovered at the destination with sufficient accuracy, even if only parts of the encoded data survive the congestion losses. The encoding is based on spatial use of erasure coding, while the recovery is based on Dictionary-based Compressed Sensing. We term this approach Congestion-Tolerant Framework. Our work is specifically targeted at the paradigm of IoT that promises to provide wide-area sensory connectivity to devices on a massive scale, interconnecting billions of such devices. Our framework is applicable to both, real-time and non-real-time IoT networking paradigms.
作者：
Walter HintonPhilip SciutoLynn OrlandoAlain DufauxCaryl JonesHGST
a Western Digital Company. 3403 Yerba Buena Road San Jose California 95135 HGST
a Western Digital Company. 3403 Yerba Buena Road San Jose California 95135 Metamedia Center
École Polytechnique Fédérale de Lausanne Route Cantonale 1015 Lausanne Switzerland Metamedia Center
École Polytechnique Fédérale de Lausanne Innovation Park Building J 1015 Lausanne Switzerland
With companies transferring and collecting vast amounts of digital information being recorded daily, studios are tasked with finding long-term sustainable solutions to create and maintain digital libraries that suppor...
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ISBN:
(纸本)9781510823389
With companies transferring and collecting vast amounts of digital information being recorded daily, studios are tasked with finding long-term sustainable solutions to create and maintain digital libraries that support content retention. Even as LTO-5 moves to LTO-7, and beyond, higher storage capacity and faster transfer rates using tape technology are not enough to rival the capacity and ease of digital storage systems. This session will look at how Object Storage/erasure Codes and Ultra-High-Density HDD options are challenging the traditional long-term data storage model and driving a new, more powerful model for archived storage.
High-density storage servers (HDSSes), which pack many disks into single servers, are currently used in data centers to save costs (power, cooling, etc). erasure coding, which stripes data and provides high availabili...
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ISBN:
(纸本)9781450397339
High-density storage servers (HDSSes), which pack many disks into single servers, are currently used in data centers to save costs (power, cooling, etc). erasure coding, which stripes data and provides high availability guarantees, is also commonly deployed in data centers at lower cost than replication. However, when applying erasure coding to a single HDSS, we find that erasure coding’s state-of-the-art studies that improve repair performance in parallel mainly use multiple servers’ sufficient footprint, which is yet quite limited in the single HDSS, thus leading to a memory-competition issue for disk failure recovery. In this paper, for a single HDSS, we analyze its disk failure recovery’s parallelism which exists within each stripe (intra-stripe) and between stripes (inter-stripe), observe that the intra-stripe and inter-stripe parallelisms are mutually restrictive, and explore how they affect the disk failure recovery time. Based on the observations, we propose, for the HDSS, partial stripe repair (HD-PSR) schemes which exploit parallelism in both active and passive ways for single-disk recovery. We further propose a cooperative repair strategy to improve multi-disk recovery performance. We prototype HD-PSR and show via Amazon EC2 experiments that the recovery time of a single-disk failure and a multi-disk failure can be reduced by up to 71.7% and 52.5%, respectively, over existing erasure-coded repair scheme in high-density storage.
Data loss in wireless sensing applications is inevitable and while there have been many attempts at coping with this issue, recent developments in the area of Compressive Sensing (CS) provide a new and attractive pers...
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ISBN:
(纸本)9781424458363
Data loss in wireless sensing applications is inevitable and while there have been many attempts at coping with this issue, recent developments in the area of Compressive Sensing (CS) provide a new and attractive perspective. Since many physical signals of interest are known to be sparse or compressible, employing CS, not only compresses the data and reduces effective transmission rate, but also improves the robustness of the system to channel erasures. This is possible because reconstruction algorithms for compressively sampled signals are not hampered by the stochastic nature of wireless link disturbances, which has traditionally plagued attempts at proactively handling the effects of these errors. In this paper, we propose that if CS is employed for source compression, then CS can further be exploited as an application layer erasure coding strategy for recovering missing data. We show that CS erasure encoding (CSEC) with random sampling is efficient for handling missing data in erasure channels, paralleling the performance of BCH codes, with the added benefit of graceful degradation of the reconstruction error even when the amount of missing data far exceeds the designed redundancy. Further, since CSEC is equivalent to nominal oversampling in the incoherent measurement basis, it is computationally cheaper than conventional erasure coding. We support our proposal through extensive performance studies.
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